Practical use of analytically derived runoff models based on rainfall point processes (original) (raw)
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Analytically Derived Runoff Models Based on Rainfall Point Processes
Water Resources Research, 1990
This work introduces four stochastic and lumped in space rainfall-runoff models. They arise combining two stochastic rainfall models with two conceptualizations of a basin's response. Rainfall is given by rectangular pulses whose arrivals are described by Poisson and Neyman-Scott models. Runoff is obtained routing such pulses via two alternative conceptuali zations, both based on simple linear reservoirs. General expressions for the first and second order moments are given for both the instantaneous and locally averaged processes. The mathematical structure of the alternative parameterizations is illustrated via computer simula tions. Then, the model's ability to be used in practicat applications is tested by means of two real case studies. :*
Rectangular pulses point process models for rainfall: Analysis of empirical data
Journal of Geophysical Research, 1987
A detailed analysis of some rainfall data from Denver, Colorado, is carried out at different levels of aggregation which range from 1 to 24 hours. Two classes of models are then fitted to the data. In the first class of models, storm events arise in a Poisson process, each such event being associated with a period of rainfali of random duration and constant but random intensity. Total rainfall intensity is formed by adding the contributions from all storm events. In the sec•ond class of models, storms arise in a Poisson process, each storm giving rise to a cluster of rain cells and each cell having a random duration and constant but random intensity. The estimation of the model parameters is performed through the firstand second-order moments of the cumulative rainfall process at different levels of aggregation. It is found that the first class of models gives a poo.r fit at levels of aggregation different from the one at which the model parameters are estimated. Cluster-based models are able to take account of the cumulative rainfall characteristics over a range of time scales from 1 to 24 hours without ch. anging the model parameters. These models also perform well in regard tO the extreme values of rainfall .for different periods of aggregation and the time concentration of total rainfall. Assume that a hydrologist has an hourly r•infall record at a certain station. Obviously, from this record he 'also has available the past history of rainfall at levels of aggregation multipies of 1 hour. All of these aggregated records should obviously be related, since they all come from the same contihuous rainfall process in time. Nevertheless, to the naked eye the aggregated processes look very differently, with quite dissimilar scale structures. Faced with the task of bringing order to the past history, two different outlooks could •be taken. One outlook is. to define a set of properties of the natural ph0nomena which allow the building of a mathematical $t•ructure which statistically matches the main characteristics of the past history at a particular level of aggregation. The usefulness of this construction lies in its potential ability to adequately describe other properties of the natural process which Were not explicitly included in the building Of the mathematical structure. The modeler is content to restrict his inferences and deductions to the particular level of aggregation he is working with. A secon, d, and obviously much more ambitious outlook, is to attempt the construction of a model from whose mathematical •tructure will flow the main statistical features of the past history through a continuum of levels of aggregation. If such a model were successful, the hydrologist could then aggregate or disaggregate the results produced by the •model to obtain inferences and deductions at different time scales. This
Comparison of six rainfall-runoff modelling approaches
Journal of Hydrology, 1993
Six rainfall-runoff modelling approaches — simple polynomial equation, simple process equation (tanh equation), simple time-series equation (Tsykin equation), complex time-series model (IHACRES), simple conceptual model (SFB) and complex conceptual model (MODHYDROLOG) — are compared in this paper with the models used to simulate daily, monthly and annual flows in eight unregulated catchments. The complex conceptual model gives, by far, the best simulation of daily high and low flows, and can estimate adequately daily flows for the wetter catchments. It can provide satisfactory estimates of monthly and annual catchment yields in almost all catchments. However, the time-series approaches and the simple conceptual model can also provide adequate estimates of monthly and annual yields in the wetter catchments. As it is much easier to use these approaches than the complex conceptual model, the simpler methods may be used to estimate monthly and annual runoff in the wetter catchments.
Sensitivity of monthly rainfall-runoff models to input errors and data length
Hydrological Sciences Journal, 1994
Two problems are addressed which arise when using monthly water balance models as an aid to making decisions in water resources engineering: what is the influence of data errors on model performance, and what is the data length required in order to obtain reliable models? Two previously defined types of models are used: in PE type models the input series are precipitation and potential évapotranspiration; in P type models the only input is precipitation. The main conclusions are:
Water Resources Research, 2016
Hydrologic models have potential to be useful tools in planning for future climate variability. However, recent literature suggests that the current generation of conceptual rainfall runoff models tend to underestimate the sensitivity of runoff to a given change in rainfall, leading to poor performance when evaluated over multiyear droughts. This research revisited this conclusion, investigating whether the observed poor performance could be due to insufficient model calibration and evaluation techniques. We applied an approach based on Pareto optimality to explore trade-offs between model performance in different climatic conditions. Five conceptual rainfall runoff model structures were tested in 86 catchments in Australia, for a total of 430 Pareto analyses. The Pareto results were then compared with results from a commonly used model calibration and evaluation method, the Differential Split Sample Test. We found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that models may be more capable under changing climatic conditions than previously thought. Of the 282[347] cases of apparent model failure under the split sample test using the lower [higher] of two model performance criteria trialed, 155[120] were false negatives. We discuss potential causes of remaining model failures, including the role of data errors. Although the Pareto approach proved useful, our aim was not to suggest an alternative calibration strategy, but to critically assess existing methods of model calibration and evaluation. We recommend caution when interpreting split sample results.
An Overview of Rainfall-Runoff Model Types
2018
This paper aims to inform the audience of the strengths and weaknesses of various rainfallrunoff models. Runoff plays an important role in the hydrological cycle by returning excess precipitation to the oceans and controlling how much water flows into water systems. Water resource managers use runoff data from models to help understand, control, and monitor the quality and quantity of water resources. Access to runoff data can be time consuming and restrictive. The goal of the USEPA’s Hydrologic Micro Service (HMS) project is to develop a collection of interoperable water quantity and quality modeling components that leverage existing internet-based data sources and sensors via a web service. Each component may have multiple implementations, ranging from coarse to detailed levels of physical process modeling. Each rainfall-runoff model contains algorithms that control the calculation of runoff. Models can be categorized by the structure and spatial processing of these algorithms int...
Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements
Water Resources Management, 2010
Among several hydrological models developed over the years, the most widely used technique for estimating direct runoff depth from storm rainfall i.e., the United States Department of Agriculture (USDA) Soil Conservation Service’s (SCS) Curve Number (CN) method was adopted in the present study. In addition, the Muskingum method, which continues to be popular for routing of runoff in river network, was used in the developed model to route surface runoffs from different subbasin outlet points up to the outlet point of the catchment. SCS CN method in combination with Muskingum routing technique, however, required a detailed knowledge of several important properties of the watershed, namely, soil type, land use, antecedent soil water conditions, and channel information, which may not be readily available. Due to this complexity of semi-distributed conceptual approach (SCS CN method) and non-linearity involved in rainfall-runoff modeling, researchers also attempted another less data requiring approach for runoff prediction, i.e., the neural network approach, which is inherently suited to problems that are mathematically difficult to describe. The purpose of this study was to compare the rainfall-runoff modeling performance of semi-distributed conceptual SCS CN method (in combination with Muskingum routing technique) with that of empirical ANN technique. The models were coded in C language and to make them user friendly, a Graphical User Interface (GUI) was also developed in Visual Basic 6.0. The developed models were tested for Kangsabati catchment, situated in the western part of West Bengal, India. Monsoon data of 1996 to 1999 were used for calibration of the models whereas they were validated for another four years (1987, 1989, 1990, and 1993) monsoon data. Modeling efficiency (ME) and coefficient of residual mass (CRM) were used as performance indicators. Results indicated that for Kangsabati catchment, the empirical runoff prediction approach (ANN technique), in spite of requiring much less data, predicted daily runoff values more accurately than semi-distributed conceptual runoff prediction approach (SCS CN method).
Selection of an appropriately simple storm runoff model
Hydrol Earth Syst Sci, 2010
An appropriately simple event runoff model for catchment hydrological studies was derived. The model was selected from several variants as having the optimum balance between simplicity and the ability to explain daily observations of streamflow from 260 Australian catchments (23-1902 km2). Event rainfall and runoff were estimated from the observations through a combination of baseflow separation and storm flow recession analysis, producing a storm flow recession coefficient (kQF). Various model structures with up to six free parameters were investigated, covering most of the equations applied in existing lumped catchment models. The performance of alternative structures and free parameters were expressed in Aikake's Final Prediction Error Criterion (FPEC) and corresponding Nash-Sutcliffe model efficiencies (NSME) for event runoff totals. For each model variant, the number of free parameters was reduced in steps based on calculated parameter sensitivity. The resulting optimal model structure had two or three free parameters; the first describing the non-linear relationship between event rainfall and runoff (Smax), the second relating runoff to antecedent groundwater storage (CSg), and a third that described initial rainfall losses (Li), but which could be set at 8 mm without affecting model performance too much. The best three parameter model produced a median NSME of 0.64 and outperformed, for example, the Soil Conservation Service Curve Number technique (median NSME 0.30-0.41). Parameter estimation in ungauged catchments is likely to be challenging: 64% of the variance in kQF among stations could be explained by catchment climate indicators and spatial correlation, but corresponding numbers were a modest 45% for CSg, 21% for Smax and none for Li, respectively. In gauged catchments, better estimates of event rainfall depth and intensity are likely prerequisites to further improve model performance.
Journal of Hydrology, 1996
The relationship between the spatial variability of rainfall and catchment response is investigated by conducting experiments with a stochastic rainfall field model and a physically based distributed modelling system, the Systeme Hydrologique Europeen (SHE), both of which are calibrated for a small upland catchment. The development and calibration of the rainfall field model is described in Part 1 of this paper, and the experiments with simulated rainfall fields and the SHE catchment model are described in Part 2. The rainfall field model is based on the use of the Turning Bands Method (TBM) incorporating a fractionally differenced line process to generate Gaussian random fields with a specified space-time correlation structure which can be isotropic or anisotropic. A transformation is then applied to the Gaussian field to reproduce the non-stationary temporal structure and skewed marginal distribution of observed rainfall. The transformed field is then propagated in space with the required velocity. The model is calibrated using hourly data for ten storms observed at three sites in the upper Wye catchment (area 10.55 km*) at Plynlimon, Wales. As rainfall over the catchment exhibits significant variation with altitude (and other factors), an altitude correction factor is applied to the simulated rainfall fields. Comparisons of the means, variances, skewnesses, cross-and autocorrelation functions of observed and simulated storms at the sampling points show good agreement, and realistic spatial patterns are observed in the simulated fields. A procedure is developed and applied for generating conditional simulations whereby the historical storm rainfall at the sampling points can be reproduced exactly in simulated fields, thus allowing several realizations of the unobserved spatial variability of rainfall at all other points in the catchment to be generated for any historical storm.
An evaluation of three lumped conceptual rainfall-runoff models at catchment scale
Role of hydrology in managing consequences of a changing global environment, 2010
Lumped conceptual rainfall runoff models have been widely used in hydrology for many years. These models are usually able to describe most important processes in a catchment through a set of solvable equations. Thus, in many cases they are preferably over full physically-based models since they have such advantages: basic physicallybased and simplicity. However, many parameters of these models can not always be directly measured due to the fact that the conceptual models are lumped on catchment scales. Even though the model structure can be very detailed, the modelled results are possibly meaningless if the model parameters are poorly specified. The usefulness of the hydrological model relies on how well the model is calibrated. Three lumped conceptual rainfall-runoff models were presented and compared in this paper: NAM (DHI), FEH (UK), and TVM (a simplified model was developed by the author). If the NAM and TVM models are representative for continuous modelling, then the FEH model is event-based type. These three different models were applied to the Bradford catchment (UK) on a seasonal basis (summer and winter) with a time step of hourly or quarter hourly. The procedure of model calibration was presented. Model validation was performed together with statistical analysis. It has been shown in the study that overall the hydrological models represented in the paper give reasonable results in terms of accuracy. However, the selection of models for particular catchments should be based on data availability, project objective and model structure.